Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research.

多级结构方程建模 (MSEM) 允许研究人员通过分解组内和组间变异，同时在多个级别对潜在因子结构进行建模。然而，采样率（即从每组采样的病例比例）对 MSEM 模型结果的影响程度仍然未知。本文探讨了 MSEM 中采样率的变化如何影响 2 级 (L2) 潜在构造的测量。具体来说，我们在双潜 MSEM 模型的背景下使用两步蒙特卡罗模拟研究调查了采样率是否与聚合 L2 构造测量和估计中的偏差和可变性相关。调查结果表明，虽然较低的采样率与增加的偏差、标准误差和均方根误差有关，这些错误的总体规模可以忽略不计，这使得双重潜在模型成为研究人员的一个有吸引力的选择。进一步提供了一个使用实证调查数据的应用实例来说明模型的应用和解释。最后，我们考虑了各种采样率对 MSEM 研究设计的影响，特别关注教育研究。